Assignmet 3 - House pricing
Por: Jéssica Nascimento • 8/9/2015 • Trabalho acadêmico • 1.196 Palavras (5 Páginas) • 270 Visualizações
UNIVERSITY OF REGINA
FACULTY OF ENGINEERING
ENIN 880AJ – Spring/Summer 2014
Introduction to Intelligent Systems
Dr. Rene V. Mayorga
ASSIGNMENT 3
Analysis of house prices through Fuzzy Logic
A study in Regina - SK
Jéssica Maria Nascimento Ávila - 200345095
Vinicius Henrique Martins Lara - 200345094
Vitor Oliveira Ruiz Lima - 200344680
July, 2014
A. Identify an area/field of your interest (In Intelligent Manufacturing, Industrial Engineering, Financial Engineering, Petroleum Systems Engineering, Robotics, Environmental Engineering, Software Engineering, Human-Computer Interaction/Interface, Virtual Reality, etc.) where a dynamic system takes a prominent role.
According to Western Investor (2011), Regina is facing a challenge to house its growing population. New residential developments are been done to balance the houses market and keep the prices affordable. However, the speculation of the prices are inevitable, as shown on a website statement: “Regina had an impressive 104 per cent jump in housing starts from the first six months of 2012 compared to the same period a year ago” (Fong, 2012).
This problem opens a field of research, where an established pricing system can be used to regulate the market, help in investment decision making and provide fairness in housing deals. Furthermore, according to Mello & Santello (2012), the methods used to evaluate real states are based on statistics, regressions, and mainly on agent perception, which decreases the confusability of the system. Therefore, an established pricing system can be obtained through Fuzzy Logic, utilizing criteria to make rules and determine relationships among characteristics.
B. For this particular area/field identify a characteristic decision making and/or control the problem.
In order to draw the system on MatLab using Fuzzy Logic, we need to take the most important characteristics that might change the house price, such that:
- Neighborhood (region): One of the most important criteria, the square foot price of houses inside centre to house outside centre can vary up to 8% in Regina.
- Square footage: Although the price varies depending on the region, in general larger the house, higher the price.
- Number of rooms: Independent on the square foot built, the number of rooms will always increase the price, so it is another criteria to consider.
- Realty condition: If the house is a brand new, the price will be substantially higher comparing to an old one.
- Garage spots: Usually, garage spots vary according to the house size. For a big family for example, more spots are required. Garage spots can indicate social condition either.
Those characteristics will be used to reach conditions and relationships rules to obtain a final answer, in other words, the characteristics will determine the house price through interweaving of information.
C. Establish the decision making and/or control problem under a suitable(Agents,and/or Intelligent Systems) Framework.
The decision making/or control problem will be studies through a survey. Firstly, the houses prices in Regina will be statistically analyzed, to obtain real results. To get the relations the group intend to study all the properties on sale on the internet.
In this case, we have 5 inputs that have already been mentioned (Garage spots, Number of rooms, Realty condition, Neighborhood (region) and Square footage) and one output: Final Price.
The primary idea is to classified the inputs in high, medium or low obeying the results of the survey. The output (Final Price) will be a price within the reality of the city.
Inputs:
- Criteria: Garage spots:
o 0 – Low
o 1-2 – Medium
o 3+ – High
- Criteria: Number of rooms:
o 3- – Low
o 4-6 – Medium
o 7+ – High
- Criteria: Realty condition:
o Low
o Medium
o High
- Criteria: Neighborhood (region):
o Far Away from Centre - Low
o Near to Centre - Medium
...